Machine learning generally refers to a field of computer science that is focused on enabling machines such as computers to learn without being explicitly programmed. Machine learning includes the study and construction of machine-performed algorithms or techniques that enable machines to learn from and make predictions on data. In particular, such algorithms can operate by building a model from a training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static programming instructions.
One main branch of machine learning techniques includes supervised learning techniques. Supervised learning can include inferring or learning a function or model from a training data set that includes a number of labeled examples. For example, each example in the training data set can include one or more input values (which can be expressed as a vector with a number of features, as a two dimensional matrix, or in other formats) and one or more desired output values (which can also be called supervisory signals). Typically, supervised training data is labeled using known ground truth information that provides the input values and the output values. A supervised machine learning algorithm can analyze the training data and produce an inferred model, which can then be used for mapping or making predictions on new, unlabeled examples.
However, in many scenarios, labeled data and/or ground truth data can be difficult and/or costly to obtain. For example, manual labeling of data can require a human to determine and provide a large amount of labels to an enormous number of training examples, which can require an inordinate amount of time and expense. In addition, providing manual labels can be conceptually challenging because, in some instances, the human labeler(s) can be required to agree ahead of time exactly what labels are available for use. Even assuming agreement on the vocabulary of available labels, labelers may not agree on which label to apply or how or when to apply the label to a given training example.